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Conventional artificial intelligence (AI) has demonstrated its ability to enhance the communication performance by improving channel estimation, signal detection, and error correction, leveraging its strengths in analysis, compression, and feature extraction to boost these critical functions. However, as the industry progresses towards 6G, characterized by extraordinarily high data rates, extremely broad coverage, and ubiquitous network access, traditional AI may face challenges in addressing the complexities resulting from the large volume of data generated by numerous interconnected devices and the real-time requirements in dynamic and unpredictable environments. Generative foundation models (GFMs), marked by innovative models like generative pretrained transformers (GPTs), has emerged as a paradigm shift in overcoming the limitations of traditional AI approaches for communication systems. Specifically, GFMs are powerful for learning the input signal distributions, modeling complex channel distributions, and inferring latent variables for blind channel equalization. 

Despite the significant potential of integrating GFMs with communication, several challenges arise. First, ensuring the security and privacy of communication is a paramount challenge since the GFM system is vulnerable to adversarial attacks in potentially unsecured environments with increasingly sensitive information. Moreover, the large-scale nature and complexity of GFMs introduce difficulties in real-time adaptation to the highly dynamic context of communication systems while maintaining high-performance levels without prior channel knowledge. Accordingly, addressing these challenges is essential for exploiting the full potential of GFMs in enhancing the communication system performance.

Topics of interest include, but are not limited to:

• New framework of GFM, e.g., LLMs, Large Language Models (LLMs), Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Normalizing Flows (NFs), Diffusion Models, and Transformers for wireless physical layer communication
• New theories and algorithms of GFM for wireless physical layer communications
• GFM-based channel equalization in wireless physical layer communications
• GFM-empowered channel modeling in wireless physical layer communications
• Wireless signal processing supported by GFM
• GFM-assisted channel estimation for wireless physical layer communications
• Communication security enhancement by GFMs
• GFM-based approach for IRS
• GFM for massive MIMO and extremely large MIMO beamforming
• Near-field communications enabled by GFM
• GFM for joint source-channel coding
• GFM-empowered channel state information (CSI) feedback
• GFM for radio map and channel delay estimation
• GFM-based radio resource management 
• GFM-empowered spectrum sharing and allocation in communication systems 
• Adaptive modulation and coding schemes supported by GFM
• GFM for dynamic spectrum access and cognitive radio networks
• GFM-enabled power control and energy-efficient communications
• GFM for network slicing
• GFM-based scheduling and MAC layer protocols for 5G/6G networks
• Cross-layer optimization and design using GFM
• GFM-enabled access control and admission control mechanisms
• Testbed and real experiments of GFM implementation for communications

Submission Guidelines

Prospective authors should prepare their submissions in accordance with the rules specified in the Information for Authors of the IEEE Communication Magazine guidelines. 

All manuscripts to be considered for publication must be submitted by the deadline through Author Portal. Select the “SI-2404/Generative Foundation Models for Communications” topic from the drop-down menu of Topic/Series titles. Please observe the dates specified here below noting that there will be no extension of submission deadline.

Important Dates

Manuscript Submission Deadline: 30 December 2024
Initial Decision Date: 3 March 2025
Revised Manuscript Due: 20 April 2025
Final Decision Date: 31 May 2025
Final Manuscript Due: 30 June 2025
Publication Date: September 2025

Guest Editors

Dusit Niyato (Lead Editor)
Nanyang Technological University, Singapore

Geng Sun
Jilin University, China

Octavia A. Dobre
Memorial University, Canada

Geoffrey Li
Imperial College London, UK